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Physics-informed machine learning

Machine learning used to represent physics-based and/or engineering models

Papers

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Showing 151–160 of 192 papers

TitleStatusHype
Grid-SiPhyR: An end-to-end learning to optimize framework for combinatorial problems in power systems—0
Towards Size-Independent Generalization Bounds for Deep Operator NetsCode0
Scalable algorithms for physics-informed neural and graph networks—0
Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experimentCode0
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning—0
Calibrating constitutive models with full-field data via physics informed neural networks—0
Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Network—0
Numerical Approximation in CFD Problems Using Physics Informed Machine Learning—0
Towards Model Reduction for Power System Transients with Physics-Informed PDE—0
A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs—0
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